微型机与应用2012，Vol.31Issue(20)：74-76,3.
一种改进的动态K—means聚类算法
An improved dynamic K-means clustering algorithm
摘要
Abstract
There are great impacts on traditional K-means algorithm results of clustering for initial cluster centers. A new im- proved K-means algorithm is proposed. A new method for selecting initial cluster centers according to the inner class distance of samples which dynamically adjust the distance between clustering. It not only can nake the cluster centers as dispersed as possible and highly representative ,but can avoid K-means algorithm into local optimum effectively. The improved algorithm is done experi- ments on data of UCI data set, the results show that improved algorithm can improve the clustering accuracy.关键词
K-means/聚类算法/初始聚类中心/动态聚类Key words
K-means/clustering algorithm/initial clustering centers/dynamic clustering分类
信息技术与安全科学引用本文复制引用
詹辉煌,朱敏琛..一种改进的动态K—means聚类算法[J].微型机与应用,2012,31(20):74-76,3.基金项目
福建省自然科学基金 （）
福建省科技计划重点项目 （）
